Client Report - Project 0: Introduction

Table of contents

• QUESTION|TASK 1
• QUESTION|TASK 2

import pandas as pd
import polars as pl
import numpy as np
from lets_plot import *

LetsPlot.setup_html(isolated_frame=True)

# Learn morea about Code Cells: https://quarto.org/docs/reference/cells/cells-jupyter.html

# Include and execute your code here
from palmerpenguins import load_penguins

QUESTION|TASK 1

Include the tables created from PY4DS: CH2 Data Visualization used to create the above chart (Hint: copy the code from 2.2.1. The penguins data frame and paste each in the cells below)

# Include and execute your code here

df = load_penguins()
df

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007
...	...	...	...	...	...	...	...	...
339	Chinstrap	Dream	55.8	19.8	207.0	4000.0	male	2009
340	Chinstrap	Dream	43.5	18.1	202.0	3400.0	female	2009
341	Chinstrap	Dream	49.6	18.2	193.0	3775.0	male	2009
342	Chinstrap	Dream	50.8	19.0	210.0	4100.0	male	2009
343	Chinstrap	Dream	50.2	18.7	198.0	3775.0	female	2009

344 rows × 8 columns

include figures in chunks and discuss your findings in the figure.

# Include and execute your code here
df.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	male	2007
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	female	2007
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	female	2007
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN	2007
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	female	2007

QUESTION|TASK 2

Recreate the example charts from PY4DS: CH2 Data Visualization of the textbook. (Hint: copy the chart code from 2.2.3. Creating a Plot, one for each cell below)

# Include and execute your code here
scat_plt1 = ggplot(
  data = df,
  mapping = aes(x = "flipper_length_mm", y = "body_mass_g")
) + geom_point(
  mapping = aes(color = "species", shape = "species")
  ) + geom_smooth(method = "lm") + labs(
    title="Body mass and flipper length",
    subtitle="Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
    x="Flipper length (mm)",
    y="Body mass (g)",
    color="Species", #This simply defines the label in the legend for color
    shape="Species"
  )

scat_plt1

As can be seen in the chart above, the Gentoo penguins are heavier and have a larger flipper length than either the Adelie or Chinstrap penguins.

# Include and execute your code here
scat_plt2 = ggplot(
  data=df, mapping=aes(x="bill_length_mm", y="bill_depth_mm")
) + geom_point(
  mapping = aes(color = "island")
) + geom_smooth(
  se = False
) + labs(
  title="Bill length and depth",
  subtitle="Dimensions for islands Torgersen, Biscoe, and Dream",
  x="Bill length (mm)",
  y="Bill depth (mm)",
  color="Island"
)

scat_plt2

All penguins on the Torgersen Island have a bill length less than about 45mm. The other two islands ahve more scattered bill lengths. However, the majority of penguins with a bill depth less than 16mm are on the Biscoe island.

# Include and execute your code here
box_plt = ggplot(
  data=df, mapping=aes(x="species", y="bill_depth_mm")
) + geom_boxplot(
  mapping = aes(color = "species")
) + labs(
  title="Specie bill depth",
  subtitle="Quartiles of bill depth for Adelie, Gentoo, and Chinstrap Penguins",
  x="Species",
  y="Bill depth (mm)",
  color="Species"
)

box_plt

Chinstrap and Adelie penguins have deeper bills than Gentoo penguins. However, Chinstrap penguins have a tighter distribution of bill depth.

# Include and execute your code here
scat_plt3 = ggplot(
  df, aes(x="flipper_length_mm", y="body_mass_g")
) + geom_point(
  mapping = aes(color = "species")
) + labs(
  title="Flipper length and body mass",
  subtitle="Relationship of flipper length and body mass",
  x="Flipper length (mm)",
  y="Body mass (g)",
  color="Species"
)

scat_plt3

# Include and execute your code here
bar_plt = ggplot(
  df, aes(x="species")
) + geom_bar(
  mapping = aes(fill = "species")
) + labs(
  title="Penguin Species",
  subtitle="Counts of Adelie, Gentoo, and Chinstrap Penguins",
  x="Species",
  y="Count",
  fill="Species"
)

bar_plt
# if the aesthetic axis is changed between x or y it changes the orientation of the bar graph from verticle to horizontal.
# fill makes more sense as a coloring option than color for the bar graph, because color is merely the border around the bars where fill is the color of the bars

This bar chart showing the counts of penguins by species can be rotated by simply changing the aesthetic axis between x or y. Coloring bar charts merely colors the outline of the bars whereas fill changes the color of the bars themselves making it a more useful option for coloring in this case.

ggsave(scat_plt1, filename="scat_plt1.svg")
ggsave(scat_plt2, filename="scat_plt2.svg")
ggsave(scat_plt3, filename="scat_plt3.svg")
ggsave(bar_plt, filename="bar_plt.svg")
ggsave(box_plt, filename="box_plt.svg")

'C:\\Users\\Zhenterigone\\Documents\\School\\Data_Science_Programming\\unit0\\lets-plot-images\\box_plt.svg'